The invention discloses a personalized multi-view federal recommendation
system, which comprises a central
server and a plurality of user clients, and any user
client comprises a training module and a prediction module; wherein the training module comprises a data distribution sub-module, a gradient calculation sub-module, a gradient aggregation sub-module, a model updating sub-module, a model
fine tuning sub-module, a user
data warehouse and an article
data warehouse which cooperate with one another to complete execution of a training
algorithm, and a user sub-model and an article sub-model are obtained; and the prediction module comprises a semantic calculation sub-module, an interactive calculation sub-module, a probability aggregation sub-module, a probability sorting sub-module, a recommendation output sub-module, a user model warehouse and an article model warehouse which cooperate with one another to complete execution of a prediction
algorithm and obtain a recommended article sequence corresponding to any user
client. According to the method, the scene adaptability is higher, the
feature mining of the underlying model is deeper, the
data source covered by the original input is wider, and the localization
fine tuning of the
global model is better.